Prediction of relapses in patients with small vessel vasculitides: a multicenter cohort study on histopathological risk patterns

小血管炎患者复发预测:一项关于组织病理学风险模式的多中心队列研究

阅读:2

Abstract

Management of ANCA-associated vasculitis (AAV) has significantly improved, yet up to 40% of patients experience relapses, leading to worse long-term outcomes and organ damage. This multicenter cohort study investigated the prognostic value of histopathological patterns in renal AAV for predicting relapse risk and treatment response. We retrospectively analyzed 264 patients with newly diagnosed granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA) with renal involvement recruited at four tertiary rheumatology and/or nephrology centers. Baseline clinical data, disease activity scores, and kidney biopsy findings were assessed. Patients were followed for at least twelve months. An overall relapse was defined as renewed disease activity requiring changes in maintenance therapy, initiation of a new induction regimen, or an increase in prednisone dosage > 10 mg/day. A severe relapse required new immunosuppressive induction therapy. After a median follow-up of twelve months the renal domain of the Birmingham Vasculitis Activity Score (BVAS) and its subcategories were evaluated. AAV patients with moderate-to-high (> 25%) interstitial fibrosis and tubular atrophy (IFTA) had a lower risk for both, overall and severe relapses. Severe relapses occurred more frequently in older and male patients. Furthermore, we identified glomerular sclerosis (≥ 50%) to be the strongest predictor for ongoing activity in the renal BVAS domain. Histopathological features do not only help to predict renal recovery and need for dialysis but also to forecast relapse risk and renal BVAS activity. Incorporating these patterns into clinical decision-making could enable more personalized therapy approaches, highlighting the need for prospective validation.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。